Descripción del proyecto
Formación de investigadores en modelos de caja gris
Las tecnologías de movilidad están configurando el futuro al poner la experiencia humana en el centro del transporte, así como en todos los modos e infraestructuras de transporte. El próximo paso consiste en respaldar la transición en curso de la movilidad personal europea hacia sistemas de movilidad fiables, inteligentes y seguros. El proyecto financiado con fondos europeos GREYDIENT creará una red de formación innovadora para la nueva generación de investigadores noveles. La atención se centrará en los modelos de caja gris, que son una respuesta al problema urgente en cuestión, ya que están destinados a integrar de manera óptima herramientas de aprendizaje automático basadas en datos (caja negra) con modelos de simulación (caja blanca). Los investigadores noveles recibirán formación en modelización, propagación y cuantificación de variabilidades relevantes, el uso de datos masivos y métodos de aprendizaje automático, así como en la combinación óptima de enfoques basados en datos con modelos matemáticos.
Objetivo
The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately. However, the training of professional profiles in Europe who combine knowledge and experience in state-of-the-art data-driven black box and numerical white box approaches with expertise in methods for reliability and safety estimation is scarce. Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such commercialization, collaboration and entrepreneurship. This training will be organized in close cooperation with key industry stakeholders.
Ámbito científico
- social scienceseconomics and businessbusiness and managemententrepreneurship
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksdata networks
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Palabras clave
Programa(s)
Régimen de financiación
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Coordinador
3000 Leuven
Bélgica